def param_init_gru(options,
params,
prefix='gru',
nin=None,
dim=None,
hiero=False):
if nin is None:
nin = options['dim_proj']
if dim is None:
dim = options['dim_proj']
if not hiero:
W = numpy.concatenate([norm_weight(nin, dim),
norm_weight(nin, dim)], axis=1)
params[prfx(prefix, 'W')] = W
params[prfx(prefix, 'b')] = numpy.zeros((2 * dim,)).astype('float32')
U = numpy.concatenate([ortho_weight(dim),
ortho_weight(dim)], axis=1)
params[prfx(prefix, 'U')] = U
Wx = norm_weight(nin, dim)
params[prfx(prefix, 'Wx')] = Wx
Ux = ortho_weight(dim)
params[prfx(prefix, 'Ux')] = Ux
params[prfx(prefix, 'bx')] = numpy.zeros((dim,)).astype('float32')
return params
def param_init_lstm_tied(options,
params,
prefix='lstm_tied',
nin=None,
dim=None):
if nin is None:
nin = options['dim_proj']
if dim is None:
dim = options['dim_proj']
W = numpy.concatenate([norm_weight(nin, dim),
norm_weight(nin, dim),
norm_weight(nin, dim)], axis=1)
params[prfx(prefix, 'W')] = W
U = numpy.concatenate([ortho_weight(dim),
ortho_weight(dim),
ortho_weight(dim)], axis=1)
params[prfx(prefix, 'U')] = U
params[prfx(prefix, 'b')] = numpy.zeros((3 * dim,)).astype('float32')
return params